Applied Econometrics - Content Outline

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  1. Getting Started
    • The Model Specification Phase
      • The Principal of Parsimony
      • The Shrinkage Principle
    • The Generic Single-Equation Linear Regression Model
      • Components of the Model
        • Endogenous Variables, Exogenous Variables, Lagged Exogenous Variables, Lagged Endogenous Variables
        • The Disturbance (or Error) Term
        • Assumptions of Econometric Models
    • Data and Data Transformations
      • Data Types--Time-Series, Cross-Sectional, Combination
      • Getting a Feel for the Data--Plots of Key Variables,Scatter Plots, and Descriptive Statistics
      • Massaging the Data
        • Expression of Data--Nominal versus Real or Inflation-Adjusted Measures
        • Expression of Data--Total or Per Capita Terms
        • Expression of Data--Levels, Changes, or Percentage Changes
        • Use of Moving Averages or Exponential Smoothing
        • Adjustments for Seasonality
        • Imputations for Missing Data


  2. Mathematical and Statistical Considerations
    • Key Constructs in Applied Econometric Models
    • Estimation of Structural Parameters in Applied Econometrics
      • Marginal Effects, Standardized Regression Coefficients. Elasticities, and Partial Correlation Coefficients
    • Interval Estimation and the Construction of Confidence Intervals


  3. Common Tests of Hypotheses
    • Tests of Hypotheses Regarding Structural Parameters of Econometric Models
      • Statistical Distributions--Standard Normal, t, Chi-squared, and F
      • Level of Significance and p-Values
      • Goodness-of-Fit Test
      • Tests of Single Coefficients
      • Tests of Linear Combinations of Coefficients
    • Additional Tests of Hypotheses in Econometric Models
      • Test of Normality of the Residuals (the Jarque-Bera Test)
      • Ramsey Regression Specification Error Test (the Ramsey RESET Test)
      • Box-Cox Test (Test of Functional Form)
      • Granger Causality Test (Test of Precedence)
      • Hausman Test (Test of Endogeneity/Exogeneity of Explanatory Variables)


  4. Use of Indicator or Dummy Variables
    • Overview (the Representation of Qualitative Variables in Applied Econometrics)


  5. Autocorrelation or Serial Correlation
    • Definition, Prevalence, and Consequences of Serial Correlation
      • Systematic Pattern in the Residuals
      • Positive Versus Negative Autocorrelation
    • Formal Tests of Serial Correlation
      • Durbin-Watson Test
    • Solution to the Serial Correlation Problem


  6. Heteroscedasticity
    • Definition, Prevalence, and Consequences of Heteroscedasticity
      • Indigenous to the Use of Cross-Sectional Data
      • Examination of the Residuals--Graphical Depictions
    • Formal Tests of Heteroscedasticity
    • Solution to the Heteroscedasticity Problem
      • Application of Weighted Least Squares


  7. Collinearity
    • Definition, Prevalence, and Consequences of Collinearity
      • Consequences for Structural Parameter Estimates and Forecasting
    • Formal Diagnostics of Collinearity


  8. Influence Diagnostics: The Detection and Assessment of Data Outliers and Leverage Points
    • Definition and Consequences of Influence Points


  9. Structural Change and Stability of Structural Coefficients
    • Abrupt Structural Change
      • Breakpoint(s)
      • Use of Sequential Chow Tests
    • Formal Tests of Parameter Instability
      • Recursive Estimation
      • Recursive Residuals
    • Solution(s) to Structural Change or Parameter Instability
      • Attention to the Time Frame of the Econometric Analysis
      • Use of Dummy Variables


  10. Distributed Lag Models
    • Overview of Distributed Lag Models


  11. ARCH and GARCH Models
    • Overview of ARCH and GARCH Models
    • Mechanics of the Autoregressive Conditional Heteroscedasticity (ARCH) Model
      • Stability Considerations
    • Mechanics of the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Model


  12. Qualitative Choice Models
    • Overview of Qualitative Choice Models
    • Binary Choice Models--The Probit Model
    • Binary Choice Models--The Logit Model
    • Estimation of Probit and Logit Models
    • Calculation of Appropriate Marginal Effects
    • The Prediction-Success Table


  13. Censored Response Models


  14. Pooling of Time-Series and Cross-Sectional Data
    • Overview and Examples of Pooled Time-Series and Cross-Sectional Data
    • To Pool or Not to Pool?
    • Cross-Sectionally Heteroscedastic and Time-Wise Autoregressive Models
    • Fixed and Random Effects


  15. Simultaneous-Equation Models
    • Order Conditions for Identification
    • Impact, Interim, and Total Multipliers
  16. Stability Conditions of Simultaneous-Equation Models


  17. Seemingly Unrelated Regression (SUR) Models